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- 한국산업응용수학회 JOURNAL OF THE KOREAN SOCIETY FOR INDUSTRIAL AND APPLIED MATHEMATICS Journal of the Korean Society for Industrial and Applied Mathematics Vol.22 No.1
- 발행연도
- 2018.3
- 수록면
- 15 - 28 (14page)
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초록· 키워드
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Recently Machine Learning algorithms are widely used to process Big Data in various applications and a lot of these applications are executed in run time. Therefore the speed of Machine Learning algorithms is a critical issue in these applications. However the most of modern iteration Machine Learning algorithms use a successive iteration technique well-known in Numerical Linear Algebra. But this technique has a very low convergence, needs a lot of iterations to get solution of considering problems and therefore a lot of time for processing even on modern multi-core computers and clusters. Tchebychev iteration technique is well-known in Numerical Linear Algebra as an attractive candidate to decrease the number of iterations in Machine Learning iteration algorithms and also to decrease the running time of these algorithms those is very important especially in run time applications. In this paper we consider the usage of Tchebychev iterations for acceleration of well-known K-Means and SVM (Support Vector Machine) clustering algorithms in Machine Leaning. Some examples of usage of our approach on modern multi-core computers under Apache Spark framework will be considered and discussed.
목차
ABSTRACT
1. INTRODUCTION
2. BACKGROUND OF K-MEANS CLUSTERING
3. PROFILE-GUIDED TCHEBYCHEV ALGORITHM
4. PERFORMANCE OF PROFILE-GUIDED TCHEBYCHEV ALGORITHM
5. BACKGROUND OF SVM CLUSTERING
7. PERFORMANCE OF MULTI-LAYERS PROFILE-GUIDED TCHEBYCHEV SVM-SMO ALGORITHM
8. CONCLUSIONS
REFERENCES
참고문헌 (16)
참고문헌 신청
[학술지(정기간행물)] - Xindong Wu / 2008 / Top 10 Algorithms in Data Mining / Knowledge Information Systems 14 : 1 ~ 37
[학술대회논문] - Dan Pelleg / 1999 / Accelerating Exact k-means Algorithms with Geometric Reasoning / KDD '99 The Fifth International Conference on Knowledge Discovery and Data Mining : 277 ~ 281
[보고서] - Phillip Eliasson / / Efficient K-means clustering and the importance of seeding;KTH Royal Institute of Technology, School of Computer Science and Communication Reports
[학술대회논문] - Sugato Basu / 2002 / Semi-supervised Clustering by Seeding / Proceedings of the 19th International Conference on Machine Learning (ICML-2002) : 19 ~ 26
[단행본] - David Arthur / 2006 / k-means++: The advantages of Careful Seeding / Stanford InfoLab Publications
[학술대회논문] - Dan Pelleg / 1999 / Accelerating Exact k-means Algorithms with Geometric Reasoning / KDD '99 The Fifth International Conference on Knowledge Discovery and Data Mining : 277 ~ 281
[보고서] - Phillip Eliasson / / Efficient K-means clustering and the importance of seeding;KTH Royal Institute of Technology, School of Computer Science and Communication Reports
[학술대회논문] - Sugato Basu / 2002 / Semi-supervised Clustering by Seeding / Proceedings of the 19th International Conference on Machine Learning (ICML-2002) : 19 ~ 26
[단행본] - David Arthur / 2006 / k-means++: The advantages of Careful Seeding / Stanford InfoLab Publications
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